一种支持范围查询的P2P动态负载平衡算法

为支持连续数据按范围分段存放的P2P系统的动态负载平衡,使用了局部负载平衡算法和基于负载目录的负载平衡算法相结合的方法:局部负载平衡算法使负载交换只在邻居节点间进行,保证了相邻节点的负载始终处于平衡状态;基于负载目录的负载平衡算法使用负载目录克服了负载聚集时对节点进行均匀随机采样困难的问题,负载交换在负载目录中的所有节点中进行,使得系统能够以很高的概率满足全局负载平衡条件.算法分析和实验结果表明,这种方法可以取得较好的动态负载平衡效果.     

TSS-SON:一种基于结构化P2P网络路由的两级语义覆盖网络结构

提出了基于结构化P2P网络路由机制的两级语义覆盖网络结构TSS-SON.该结构根据资源主题将节点组织成主题区域,在同一主题区域内,采用基于语义相似度机制进行群的划分.该结构充分利用了结构化P2P网络的优点,解决了基于非结构化P2P网络建立的语义覆盖网络对主题群的搜索低效问题,同时克服了结构化P2P网络仅支持精确匹配查找的缺点,实现了可靠、高效的语义查询,并极大地提高了查全率.     

CDN和P2P融合的流媒体内容分发平台

CDN和P2P以不同的方式、不同的优势解决了内容分发的问题，但受其固有计算模型的制约，无论是CDN或者P2P均存在一些根本的缺点。利用CDN与P2P的互补性，发挥各自的优势，将CDN和P2P技术融合，构建一种新型的流媒体内容分发模型。     

一种基于标记和本体的分布式P2P资源获取方法

在P2P蓬勃发展的今天,人们从P2P文件交换系统中获得了不少的好处。然而Web服务因缺乏语义而不能更好地提供搜索服务,这一问题在P2P资源共享同样中存在,故难以简单地通过文本匹配的方法寻找到自己需要的资源。语义Web可以为Web服务提供灵活的查找方式,受语义Web的启发,提出一种基于标签和本体的P2P资源检索获取方法,并且讨论此方法在协作环境下的进化情况。     

PGACS:一种基于P2P架构的分组匿名通信系统模型

为使目前基于重路由机制的P2P匿名系统在用户增加时减少负载开销和管理开销,从而提高其可扩展性,提出了一种基于P2P架构的分组匿名通信系统模型--PGACS.系统中没有核心的管理节点,每个用户加入一定数量的逻辑组,并只保留其所在逻辑组成员的信息.给出了成员加入、退出的协议机制以及逻辑组的分裂合并机制,对系统的可扩展性和安全性进行了分析,同时基于前驱攻击对系统匿名性能进行了理论分析和模拟测试.结果表明,在合理选取逻辑组大小的前提下,系统能够提供与典型匿名系统Crowds相当的匿名性,同时具有良好的扩展性.     

数字认证在P2P中的应用

在很多未来网络发展的文章中,经常能看到关于P2P的字眼.P2P是未来网络的热门关键词,由于P2P很多技术及标准还处于研制和探讨的过程中,结合目前的P2P与安全中的数字认证,猜想数字认证在未来网络中的发展,提出P2PSMS,一个基于P2P的安全系统模型,描述用户在P2P网络平台中与主动节点实现密码PKI认证等过程.     

基于P2P模式的流媒体传输技术

传统流媒体系统受带宽和服务器限制,难以支持大规模并发客户.而在P2P网络中,每个接收数据的用户同时向外转发数据,这就充分利用了以往忽视的客户机资源.介绍传统C/S架构的缺陷和P2P流媒体系统的关键技术,指出P2P流媒体的未来研究方向.     

一种适合异构P2P网络的树形结构覆盖层

为实现异构网络下的P2P应用,分析了具有异构融合特征的P2P网络的特点,提出了与之相适应的基于二叉树结构的覆盖层网络(TSOHEN)的设计方法.该方法根据节点的不同功能和属性将节点分为普通节点和混合节点两大类,并为每类节点设计了适应异构特征的路由表,对各类节点的加入和离开功能设计了相应的算法,并通过混合节点实现跨网的P2P查询操作.数值和仿真结果表明,该覆盖层设计能够有效地适应异构网络的环境,树形结构也没有使得根节点和叶节点的负荷产生明显的区别,各混合节点的负载也基本平衡.在大规模节点数量的情况下,TSOHEN的各种算法仍具有良好的收敛性.     

基于P2P网络的资源搜索技术研究

对P2P资源搜索的拓扑结构和资源搜索算法等相关知识作较详细的介绍,对基于不同P2P结构的搜索算法作简单的对比和分析。并针对现有搜索算法存在的问题,提出一些解决的设想,最后对影响搜索算法的因素和解决的方法进行归纳。     

基于P2P的图书馆用户信息资源共享研究

P2P技术开创信息资源共享的新局面,节约有限社会资源,提高信息资源的使用效益。简要介绍P2P的特点及模型结构,分析基于P2P的信息资源共享的优点。     

k-supplier问题的贪婪近似算法

给定无向完全图G=(V,E)和正整数k,图G的顶点集V被划分为子集F和子集D=V-F.k-supplier问题主要研究如何寻找F中顶点数不多于k的子集S,使得S中的顶点到D中顶点的最大距离最小.研究了k-supplier问题,得到了一个近似比为3的多项式时间贪婪近似算法,并通过实例验证了该算法的有效性.     

An improved subspace selection algorithm for meshless collocation methods

Choosing data points is a common problem for researchers who employ various meshless methods for solving partial differential equations. On the one hand, high accuracy is always desired; on the other, ill‐conditioning problems of the resultant matrices, which may lead to unstable algorithms, prevent some researchers from using meshless methods. For example, the optimal placements of source points in the method of fundamental solutions or of the centers in the radial basis functions method are always unclear. Intuitively, such optimal locations will depend on many factors: the partial differential equations, the domain, the trial basis used (i.e. the employed method itself 1pt), the computational precisions, some user‐defined parameters, and so on. Such complexity makes the hope of having an optimal centers placement unpromising. In this paper, we provide a data‐dependent algorithm that adaptively selects centers based on all the other variables. Copyright © 2009 John Wiley & Sons, Ltd.     

A genetic algorithm with domain knowledge for weapon‐target assignment problems

Abstract

In this paper, a novel genetic algorithm, including domain specific knowledge into the crossover operator and the local search mechanism for solving weapon‐target assignment (WTA) problems is proposed. The WTA problem is a full assignment of weapons to hostile targets with the objective of minimizing the expected damage value to own‐force assets. It is an NP‐complete problem. In our study, a greedy reformation and a new crossover operator are proposed to improve the search efficiency. The proposed algorithm outperforms its competitors on all test problems.     

A Hybrid Genetic Algorithm for Vehicle Routing Problem with Complex Constraints

Most research on the Vehicle Routing Problem （VRP） is focused on standard conditions, which is not suitable for specific cases. A Hybrid Genetic Algorithm is proposed to solve a Vehicle Routing Problem （VRP） with complex side constraints. A novel coding method is designed especially for side constraints. A greedy algorithm combined with a random algorithm is introduced to enable the diversity of the initial population, as well as a local optimization algorithm employed to improve the searching efficiency. In order to evaluate the performance, this mechanism has been implemented in an oil distribution center, the experimental and executing results show that the near global optimal solution can be easily and quickly obtained by this method, and the solution is definitely satisfactory in the VRP application.     

A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem

As the interest of practitioners and researchers in scheduling in a multi-factory environment is growing, there is an increasing need to provide efficient algorithms for this type of decision problems, characterised by simultaneously addressing the assignment of jobs to different factories/workshops and their subsequent scheduling. Here we address the so-called distributed permutation flowshop scheduling problem, in which a set of jobs has to be scheduled over a number of identical factories, each one with its machines arranged as a flowshop. Several heuristics have been designed for this problem, although there is no direct comparison among them. In this paper, we propose a new heuristic which exploits the specific structure of the problem. The computational experience carried out on a well-known testbed shows that the proposed heuristic outperforms existing state-of-the-art heuristics, being able to obtain better upper bounds for more than one quarter of the problems in the testbed.     

A greedy MLPG method for identifying a control parameter in 2D parabolic PDEs

In this paper, we consider coefficient inverse problems, which are associated with the identification of unknown time dependent control parameter and unknown solution of two-dimensional parabolic inverse problem with overspecialization at a point in the spatial domain. After suitable finite difference approximation of time variable, an MLPG method is used for spatial discretization. To improve the efficiency of the MLPG method, a greedy algorithm is used. In fact, using the greedy algorithm, we avoid using more points from the data site than absolutely necessary and therefore, the method becomes more efficient. Comparison of the different kind of point selection and the effect of noisy data are performed for four test problems while our last test problem considers a problem with unknown solution. The results reveal that the method is efficient.     

A differential evolution algorithm for the no-idle flowshop scheduling problem with total tardiness criterion

In this paper, we investigate the use of a continuous algorithm for the no-idle permutation flowshop scheduling (NIPFS) problem with tardiness criterion. For this purpose, a differential evolution algorithm with variable parameter search (vpsDE) is developed to be compared to a well-known random key genetic algorithm (RKGA) from the literature. The motivation is due to the fact that a continuous DE can be very competitive for the problems where RKGAs are well suited. As an application area, we choose the NIPFS problem with the total tardiness criterion in which there is no literature on it to the best of our knowledge. The NIPFS problem is a variant of the well-known permutation flowshop (PFSP) scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions. First of all, a continuous optimisation algorithm is used to solve a combinatorial optimisation problem where some efficient methods of converting a continuous vector to a discrete job permutation and vice versa are presented. These methods are not problem specific and can be employed in any continuous algorithm to tackle the permutation type of optimisation problems. Secondly, a variable parameter search is introduced for the differential evolution algorithm which significantly accelerates the search process for global optimisation and enhances the solution quality. Thirdly, some novel ways of calculating the total tardiness from makespan are introduced for the NIPFS problem. The performance of vpsDE is evaluated against a well-known RKGA from the literature. The computational results show its highly competitive performance when compared to RKGA. It is shown in this paper that the vpsDE performs better than the RKGA, thus providing an alternative solution approach to the literature that the RKGA can be well suited.     

A genetic algorithm with memory for optimal design of laminated sandwich composite panels

This paper is concerned with augmenting genetic algorithms (GAs) to include memory for continuous variables, and applying this to stacking sequence design of laminated sandwich composite panels that involves both discrete variables and a continuous design variable. The term “memory” implies preserving data from previously analyzed designs. A balanced binary tree with nodes corresponding to discrete designs renders efficient access to the memory. For those discrete designs that occur frequently, an evolving database of continuous variable values is used to construct a spline approximation to the fitness as a function of the single continuous variable. The approximation is then used to decide when to retrieve the fitness function value from the spline and when to do an exact analysis to add a new data point for the spline. With the spline approximation in place, it is also possible to use the best solution of the approximation as a local improvement during the optimization process. The demonstration problem chosen is the stacking sequence optimization of a sandwich plate with composite face sheets for weight minimization subject to strength and buckling constraints. Comparisons are made between the cases with and without the binary tree and spline interpolation added to a standard GA. Reduced computational cost and increased performance index of a GA with these changes are demonstrated.     

Assessing hypermutation operators of a clonal selection algorithm for the unequal area facility layout problem

A mutation operator is critical for the performance of a clonal selection algorithm (CSA) since it diversifies the search directions and avoids early convergence to local optima. This article introduces a CSA approach for the unequal area facility layout problem (UAFLP) with flexible bay structure. A new encoding, the use of mutation types with different combinations, and different static and dynamic mutation application strategies are also proposed. In addition, a guideline in parameter optimization of the CSA is provided. An experimental study is performed on five cases of the UAFLP. It is concluded that the hypermutation types studied in this article, especially the inverse mutation followed by pairwise mutation, can be used to obtain good results within short computation times.